Comments (9)
Softmax isn't bijective. The one we have now is (maps from d to d-1 dimensional)
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See also #51.
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Betanalpha does discuss a bijective softmax by arbitrarily setting the endpoint logits. Any experience with this?
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That's supported e.g. in GPLikelihoods (see maybe also the discussion in JuliaGaussianProcesses/GPLikelihoods.jl#55).
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Good to know thanks. Though, back to my original intention, I really wish that our simplex bijector could play nicely with GPUs out of the box. Among non-NF bijectors, it seems the simplex bijector is really going to be the big challenge going in that direction. Do we have any plans on how to pursue this? It does seem to me that the softmax approach would be much easier to get this done.
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Actually, nevermind. I just wrote a stick-breaking bijector using array operations based on the implementations of numpyro and tensorflow. If this were to be added to Bijectors.jl
we'll probably have to add a CUDA array specialization. Let me know how to proceed on this.
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On Julia >= 1.9, a CUDA specialization could be put in an extension (possibly could even just be an extension with GPUArrays).
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I do have the feeling that this will have to wait until the batch operation interface is finalized. @torfjelde Do we have an expectation on when that would be?
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There are three main ways to use softmax for simplex transforms. One uses parameter expansion to retain bijectivity: f(y) = [softmax(y); logsumexp(y)]
. The other two come from compositional data analysis literature are called additive log-ratio f(y) = softmax(vcat(y, 0))
and isometric log-ratio f(y) = softmaxx(V * y)
for a particular choice of semi-orthogonal matrix V
. I'm currently testing performance of each of these versus stick-breaking.
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Related Issues (20)
- rational quadratic flows not supporting Float32 input HOT 1
- What to do with `CorrBijector` ? HOT 1
- Improve `PDVecBijector`
- Matrix factorization bijectors HOT 4
- Domain Error for VecCholeskyBijector bijector when calling logabsdetjac HOT 4
- Can't apply Bijectors.ordered to TDist() and MvTDist() HOT 1
- Incorrect bijector for heterogeneous Product distribution HOT 3
- Radial flow to a simplex HOT 5
- Stackoverflow in custom bijector HOT 2
- Missing implementation of `Bijectors.bijector` for `arraydist` distributions. HOT 1
- Bijectors.ordered and MvLogNormal interaction .. only supported for unconstrained distributions. HOT 1
- `TruncatedBijectors` not defined in `Distributions` extension
- support ProductDistribution HOT 3
- Fixes to correlation bijectors
- Improve `with_logabsdet_jacobian` performance for `SimplexBijector` HOT 1
- Tests are failing for `VecCorrBijector` in _very_ rare scenarios
- Add Tapir to Bijectors tests. HOT 2
- README links to dead docs HOT 2
- StackOverflow on calling inverse VecCorrBijector with a multidimensional array
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